Cardiac Hepcidin Expression Associates with Injury Independent of Iron
Bibliographic record
Abstract
BACKGROUND: Hepcidin regulates systemic iron homeostasis by downregulating the iron exporter ferroportin. Circulating hepcidin is mainly derived from the liver but hepcidin is also produced in the heart. We studied the differential and local regulation of hepcidin gene expression in response to myocardial infarction (MI) and/or chronic kidney disease (CKD). We hypothesized that cardiac hepcidin gene expression is induced by and regulated to severity of cardiac injury, either through direct (MI) or remote (CKD) stimuli, as well as through increased local iron content. METHODS: Nine weeks after subtotal nephrectomy (SNX) or sham surgery (CON), rats were subjected to coronary ligation (CL) or sham surgery to realize 4 groups: CON, SNX, CL and SNX + CL. In week 16, the gene expression of hepcidin, iron and damage markers in cardiac and liver tissues was assessed by quantitative polymerase chain reaction and ferritin protein expression was studied by immunohistochemistry. RESULTS: Cardiac hepcidin messenger RNA (mRNA) expression was increased 2-fold in CL (p = 0.03) and 3-fold in SNX (p = 0.01). Cardiac ferritin staining was not different among groups. Cardiac hepcidin mRNA expression correlated with mRNA expression levels of brain natriuretic peptide (β = 0.734, p < 0.001) and connective tissue growth factor (β = 0.431, p = 0.02). In contrast, liver hepcidin expression was unaffected by SNX and CL alone, while it had decreased 50% in SNX + CL (p < 0.05). Hepatic ferritin immunostaining was not different among groups. CONCLUSIONS: Our data indicate differences in hepcidin regulation in liver and heart and suggest a role for injury rather than iron as the driving force for cardiac hepcidin expression in renocardiac failure.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".